Today we start to wrap up reading the WRL research report on Swift Java compiler. We were discussing the results of the performance studies specifically from global CSE, Class Hierarchy analysis, method inlining, method splitting, field analysis. etc. and we were looking at those that dominated across most applications as well as those that helped in specific cases.
We now look at related work specifically comparing it to say Marmot which is a research compiler from Microsoft, BulletTrain which is a commercial compiler, HotSpot another commercial compiler - this one from Sun, TurboJ that uses a different language, Diwan, Cytron and others.
Marmot does a form of Class hierarchy analysis but has little intra procedural analysis, code motion and does not do instruction scheduling Moreover its IR is not SSA based as is the case with Swift. This is a significant difference and we consequently rule out other such compilers such as Jalapeño.
BulletTrain uses SSA for its IR and even does check elimination, loop unrolling, type propagation and method inlining HotSpot dynamically compiles code that is frequently executed and can use runtime profiling information. It also does method inning based on CHA. TurboJ translates to C for compilation by a C compiler and can do method resolution, inlining, CSE and code motion during the translation.
Marmot keeps memory operations in order except for promoting loads out of loops. Jalapeño builds an instruction level dependence graph that is not available until later. Diwan uses type based alias analysis but does not incorporate the results into the SSA. Cytron represents alias information in an SSA graph by explicitly inserting calls that may modify values if the associated operation may modify the value. The difference between this strategy and Swift's strategy is that Cytron can greatly increase the size of the SSA graph where as Swift not only enforces strict memory ordering via the global store inputs but also relax dependences where it can be proved that there are no aliases.
Diwan uses a form of aggregate analysis to detect when a polymorphic data structure is used in only one way. For example, it can show that a linked list of general objects may in fact be objects of a certain class or its subclasses. Swifts field analysis is more comprehensive and determines the exact types. Dolby and Chien describe an object inlining optimization for C++ programs that does context sensitive intra procedural analysis but it takes minutes as compared to seconds that Swift takes. Moreover Swift allows objects to be inlined even when there is no local reference. This is usually referred to as unboxing and exists for functional languages. Lastly, Swift has exploited field properties to do more escape analysis than others. In a way Swift claims to be considered as a complete compiler.
We will close this study of the WRL research report on Swift Java compiler with the conclusion section from the paper next.
#codingexercise
GetAllNumberRangeProductCubeRootPowerTwelve) (Double [] A)
{
if (A == null) return 0;
Return A.AllNumberRangeProductCubeRootPowerTwelve();
}
As we have seen, Swift IR simplified many aspects of the compiler, the use of SSA form made it easy to express optimizations. The Swift IR includes machine dependent operations and this allows all passes to operate directly on the SSA form. The manipulation operations on the SSA graph and CFG are common in all these passes.
Swift makes extensive use of interprocedural analysis. The most effective optimizations in Swift are method inlining, class hierarchy analysis, and global CSE. Swift also introduced field analysis and store resolution. Much of the overhead in Java appears to result from the object oriented style which results in greater memory latencies. There is room to improve optimizations and increase performance with such techniques as prefetching, co-locating objects or more aggressive object inlining.
We now look at related work specifically comparing it to say Marmot which is a research compiler from Microsoft, BulletTrain which is a commercial compiler, HotSpot another commercial compiler - this one from Sun, TurboJ that uses a different language, Diwan, Cytron and others.
Marmot does a form of Class hierarchy analysis but has little intra procedural analysis, code motion and does not do instruction scheduling Moreover its IR is not SSA based as is the case with Swift. This is a significant difference and we consequently rule out other such compilers such as Jalapeño.
BulletTrain uses SSA for its IR and even does check elimination, loop unrolling, type propagation and method inlining HotSpot dynamically compiles code that is frequently executed and can use runtime profiling information. It also does method inning based on CHA. TurboJ translates to C for compilation by a C compiler and can do method resolution, inlining, CSE and code motion during the translation.
Marmot keeps memory operations in order except for promoting loads out of loops. Jalapeño builds an instruction level dependence graph that is not available until later. Diwan uses type based alias analysis but does not incorporate the results into the SSA. Cytron represents alias information in an SSA graph by explicitly inserting calls that may modify values if the associated operation may modify the value. The difference between this strategy and Swift's strategy is that Cytron can greatly increase the size of the SSA graph where as Swift not only enforces strict memory ordering via the global store inputs but also relax dependences where it can be proved that there are no aliases.
Diwan uses a form of aggregate analysis to detect when a polymorphic data structure is used in only one way. For example, it can show that a linked list of general objects may in fact be objects of a certain class or its subclasses. Swifts field analysis is more comprehensive and determines the exact types. Dolby and Chien describe an object inlining optimization for C++ programs that does context sensitive intra procedural analysis but it takes minutes as compared to seconds that Swift takes. Moreover Swift allows objects to be inlined even when there is no local reference. This is usually referred to as unboxing and exists for functional languages. Lastly, Swift has exploited field properties to do more escape analysis than others. In a way Swift claims to be considered as a complete compiler.
We will close this study of the WRL research report on Swift Java compiler with the conclusion section from the paper next.
#codingexercise
GetAllNumberRangeProductCubeRootPowerTwelve) (Double [] A)
{
if (A == null) return 0;
Return A.AllNumberRangeProductCubeRootPowerTwelve();
}
As we have seen, Swift IR simplified many aspects of the compiler, the use of SSA form made it easy to express optimizations. The Swift IR includes machine dependent operations and this allows all passes to operate directly on the SSA form. The manipulation operations on the SSA graph and CFG are common in all these passes.
Swift makes extensive use of interprocedural analysis. The most effective optimizations in Swift are method inlining, class hierarchy analysis, and global CSE. Swift also introduced field analysis and store resolution. Much of the overhead in Java appears to result from the object oriented style which results in greater memory latencies. There is room to improve optimizations and increase performance with such techniques as prefetching, co-locating objects or more aggressive object inlining.
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